The Rise of Machine Learning in Mobile Commerce

Danielle Caley, Bijou Commerce

Author Bio

Danielle is responsible for marketing at Bijou Commerce, a mobile app company that delivers highly intuitive mCommerce apps for retailers. She joined Bijou as part of a fast-track digital graduate scheme, spending three months learning about everything from marketing to coding, and then joining the team.

Over the past decade, we’ve seen a rapid shift in consumer behaviour. The rise of the smartphone has fundamentally changed the way consumers interact with brands – increasingly opting for mobile over desktop. In fact, some have even gone so far as to say the future of ecommerce lies solely in mobile.

For retailers, this has brought both challenge and opportunity. While a mobile-first world puts brands even closer to the consumer, many have struggled to adapt fast enough and are at risk of losing out on sales. So how can they overcome the struggle? Personalisation.

According to UX Mag, “Giving users’ content tailored to their interests, needs and location is the key to making the most of mobile technology.”

Brands are already using personalisation to drive conversion rates through their web and email channels; however, mobile opportunities are often being overlooked. According to Tecmark, the average user picks up their smartphone 221 times a day, so what better place to grab their attention and deliver relevant, personalised marketing messages?

Chatbots

Last year, chatbots emerged as a big player in artificial intelligence, and they’re tipped to be the next big thing. Chatbots provide a new way to engage and sell to customers and could be a real game changer for retailers.

The biggest influence we’ve seen so far has been Facebook’s Messenger bots, which were unveiled last year. These bots can be used to answer questions, send recommendations, show product images and even respond with timely call-to-action buttons. While they didn’t quite live up to the initial hype, 2017 is expected to be the year of AI, and chatbots will undoubtedly play a big role.

Many brands have already seized the opportunity and added a customer service bot to their website, but chatbots have so much more potential, particularly with mobile. If tasks such as checking a flight or buying a new dress can be completed by having a conversation, then this could be completely revolutionary – that is, if they result in cold hard conversion and incremental revenue uplift.

And the opportunities for personalisation make this technology all the more worthwhile. Sophisticated chatbots use machine learning algorithms and natural language processing to make them respond like real people. The bots can chat with customers and actually adapt in real time by identifying their preferences and replying with a tailored response.

A few digital disrupters have already put chatbots to the test. The North Face allows customers to chat while they shop and receive messages with personalised recommendations. So far, the feature has been well received, and clearly, this exciting trend has been recognised, as according to Localytics, 80% of businesses want to be using chatbots by 2020.

Personalised Merchandising

According to Retail TouchPoints, “Inventory management is a huge challenge for retail companies. An excess of supplies leads to low turnover and decreased profitability. Yet stock-outs result in backorders, lost sales and dissatisfied customers.” A study by IHL Group commissioned by OrderDynamics reveals that these issues cost retailers a whopping $1.1 trillion dollars every year. But with advanced analytics, retailers can begin to accurately predict trends and preferences in their customer base, gaining extremely valuable insights along the way.

These data insights can also give businesses a better understanding of where to make future investments. This can help to develop a retail offering that is more tailored to the specific audience of the brand. So how do retailers get this data?

Combining algorithms with a comprehensive view of customer analytics could well be the answer. BizTech says, “Providing customers with a seamless and tailored experience is possible through leveraging real-time information to make the customer’s shopping experience relevant and instantly satisfying.”

Contextual Push Notifications

Apps are great tools for personalisation. User preferences can be saved and used to tailor the experience, and machine learning algorithms can be added to give users unique recommendations. What is most effective, however, is the ability to send personalised push notifications.

A study by Localytics revealed that push notifications sent en masse converted at 15%, whereas personalised notifications converted at a staggering 54%.

Technological advancements continue to give us even more opportunities to collect data and personalise interactions with consumers. Smartphones can track contextual factors, ranging from location to overall health, but currently, very few brands use this information. That will likely change in the near future.

And with recent developments in machine learning, brands can use algorithms for advanced user segmentation. By constantly learning and adapting to each user’s behaviour, segments can become highly specific, enabling push notifications to become more personalised and more effective. In fact, a representative from IBM’s Watson project revealed that they can now “generate a psycholinguistic profile of an individual in literally milliseconds.” Such performance is a real indicator of where we’re headed.

Final Thoughts

The common theme across these strategies is the use of machine learning. This strand of AI will bloom in 2017, disrupting the ecommerce industry (among others) and ultimately becoming a foundation for future methods of personalisation.

The other key takeaway is the power of mobile. We are now part of a mobile-first world, and retailers have much to gain if they act quickly. But remember: Don’t just jump on the bandwagon without a solid strategy in place. The brands that will succeed are those that move fast, test and learn from a base of data – not those that adopt the hottest technology just for the sake of it.

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